DS007445: ieeg dataset, 19 subjects#

Thalamocortical ictal iEEG dataset

Access recordings and metadata through EEGDash.

Citation: Saarang Panchavati, Atsuro Daida, Sotaro Kanai, Shingo Oana, Hiroya Ono, Masaki Izumi, Kikuko Kaneko, Aria Fallah, Joe X Qiao, Noriko Salamon, Raman Sankar, Corey Arnold, William Speier, Hiroki Nariai (2026). Thalamocortical ictal iEEG dataset. 10.18112/openneuro.ds007445.v1.0.2

Modality: ieeg Subjects: 19 Recordings: 66 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007445

dataset = DS007445(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007445(cache_dir="./data", subject="01")

Advanced query

dataset = DS007445(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds007445,
  title = {Thalamocortical ictal iEEG dataset},
  author = {Saarang Panchavati and Atsuro Daida and Sotaro Kanai and Shingo Oana and Hiroya Ono and Masaki Izumi and Kikuko Kaneko and Aria Fallah and Joe X Qiao and Noriko Salamon and Raman Sankar and Corey Arnold and William Speier and Hiroki Nariai},
  doi = {10.18112/openneuro.ds007445.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds007445.v1.0.2},
}

About This Dataset#

We investigated thalamocortical network dynamics using intracranial EEG (iEEG) recordings with thalamic sampling from 19 patients with focal epilepsy (1). The iEEG dataset analyzed in this study is publicly shared here. BIDS converstion was performed according to references (2) and (3). References (1) Panchavati S, Daida A, Kanai S, Oana S, Ono H, Izumi M, Kaneko K, Fallah A, Qiao JX, Salamon N, Sankar R, Arnold C, Speier W, Nariai H (2026). Distinct Spectral and Directional Thalamocortical Network Dynamics Define Focal Seizure Evolution. medRxiv, 2026 Feb 4:2026.02.03.26345480. doi: 10.64898/2026.02.03.26345480. (2) Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896 (3) Holdgraf, C., Appelhoff, S., Bickel, S., Bouchard, K., D’Ambrosio, S., David, O., … Hermes, D. (2019). iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology. Scientific Data, 6, 102. https://doi.org/10.1038/s41597-019-0105-7

Dataset Information#

Dataset ID

DS007445

Title

Thalamocortical ictal iEEG dataset

Author (year)

Panchavati2026

Canonical

Importable as

DS007445, Panchavati2026

Year

2026

Authors

Saarang Panchavati, Atsuro Daida, Sotaro Kanai, Shingo Oana, Hiroya Ono, Masaki Izumi, Kikuko Kaneko, Aria Fallah, Joe X Qiao, Noriko Salamon, Raman Sankar, Corey Arnold, William Speier, Hiroki Nariai

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007445.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL

Copy-paste BibTeX
@dataset{ds007445,
  title = {Thalamocortical ictal iEEG dataset},
  author = {Saarang Panchavati and Atsuro Daida and Sotaro Kanai and Shingo Oana and Hiroya Ono and Masaki Izumi and Kikuko Kaneko and Aria Fallah and Joe X Qiao and Noriko Salamon and Raman Sankar and Corey Arnold and William Speier and Hiroki Nariai},
  doi = {10.18112/openneuro.ds007445.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds007445.v1.0.2},
}

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings
  • Subjects: 19

  • Recordings: 66

  • Tasks: 1

Channels & sampling rate
  • Channels: 140 (10), 138 (10), 83 (6), 265 (6), 202 (5), 216 (5), 162 (4), 203 (3), 112 (3), 68 (2), 49 (2), 81 (2), 263, 120, 201, 139, 111, 124, 261, 137

  • Sampling rate (Hz): 200.0 (42), 2000.0 (17), 200.00000000000003 (6), 1999.9999999999998

  • Duration (hours): 73.70927875000001

Tags
  • Pathology: Epilepsy

  • Modality: Other

  • Type: Clinical/Intervention

Files & format
  • Size on disk: 50.5 GB

  • File count: 66

  • Format: BIDS

License & citation
  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007445.v1.0.2

Provenance

API Reference#

Use the DS007445 class to access this dataset programmatically.

class eegdash.dataset.DS007445(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Thalamocortical ictal iEEG dataset

Study:

ds007445 (OpenNeuro)

Author (year):

Panchavati2026

Canonical:

Also importable as: DS007445, Panchavati2026.

Modality: ieeg; Experiment type: Clinical/Intervention; Subject type: Epilepsy. Subjects: 19; recordings: 66; tasks: 1.

Parameters:
  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds007445 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007445 DOI: https://doi.org/10.18112/openneuro.ds007445.v1.0.2

Examples

>>> from eegdash.dataset import DS007445
>>> dataset = DS007445(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path, overwrite=False)[source]#

Save the dataset to disk.

Parameters:
  • path (str or Path) – Destination file path.

  • overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None

See Also#